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Poster C17 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
EEG-Features for Generalized Deepfake Detection
Arian Beckmann1, Tilman Stephani2, Felix Klotzsche2, Yonghao Chen2, Simon M. Hofmann2, Arno Villringer2, Michael Gaebler2, Vadim Nikulin1, Sebastian Bosse1, Peter Eisert1,3, Anna Hilsmann1; 1Fraunhofer Heinrich-Hertz-Institute, 2Max-Planck-Institute for Human Cognitive and Brain Sciences, 3Humboldt-Universität zu Berlin
Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured from the neural processing of a human participant who viewed and categorized Deepfake stimuli from the FaceForensics++ datset. These measurements serve as input features to a binary support vector classifier, trained to discriminate between real and manipulated facial images. We examine whether EEG data can inform Deepfake detection and also if it can provide a generalized representation capable of identifying Deepfakes beyond the training domain. Our preliminary results indicate that human neural processing signals can be successfully integrated into Deepfake detection frameworks and hint at the potential for a generalized neural representation of artifacts in computer generated faces. Moreover, our study provides next steps towards the understanding of how digital realism is embedded in the human cognitive system, possibly enabling the development of more realistic digital avatars in the future.
Keywords: deepfake eeg perception realism